In ocean acoustics, many types of optimizations have been employed to locate acoustic sources and estimate the properties of the seabed. How these tasks can take advantage of recent advances in deep learning remains as open questions, especially due to the lack of labeled field data. In this work, a Convolutional Neural Network (CNN) is used to find seabed type and source range simultaneously from 1 s pressure time series from impulsive sounds. Simulated data are used to train the CNN before application to signals from a single hydrophone signal during the 2017 Seabed Characterization Experiment. The training data includes four seabeds representing deep mud, mud over sand, sandy silt, and sand, and a wide range of source parameters. When applied to measured data, the trained CNN predicts expected seabed types and obtains ranges within 0.5 km when the source-receiver range is greater than 5 km, showing the potential for such algorithms to address these problems.

中文翻译：

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使用卷积神经网络估算脉冲时间序列的海床和距离。

在海洋声学中，已经采用了许多类型的优化来定位声源并估计海床的属性。这些任务如何利用深度学习的最新进展仍然是一个悬而未决的问题，特别是由于缺少标记的现场数据。在这项工作中，使用卷积神经网络（CNN）从脉冲声音的1 s压力时间序列中同时查找海床类型和震源范围。在2017年海床表征实验期间，模拟数据用于训练CNN，然后将其应用于来自单个水听器信号的信号。训练数据包括代表深层泥浆，沙上的泥浆，沙质粉砂和沙子的四个海床，以及广泛的源参数。当应用于测量数据时，受过训练的CNN会预测预期的海床类型并获得0范围内的范围。